29 research outputs found

    When and how to use Q methodology to understand perspectives in conservation research.

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    Understanding human perspectives is critical in a range of conservation contexts, for example, in overcoming conflicts or developing projects that are acceptable to relevant stakeholders. The Q methodology is a unique semiquantitative technique used to explore human perspectives. It has been applied for decades in other disciplines and recently gained traction in conservation. This paper helps researchers assess when Q is useful for a given conservation question and what its use involves. To do so, we explained the steps necessary to conduct a Q study, from the research design to the interpretation of results. We provided recommendations to minimize biases in conducting a Q study, which can affect mostly when designing the study and collecting the data. We conducted a structured literature review of 52 studies to examine in what empirical conservation contexts Q has been used. Most studies were subnational or national cases, but some also address multinational or global questions. We found that Q has been applied to 4 broad types of conservation goals: addressing conflict, devising management alternatives, understanding policy acceptability, and critically reflecting on the values that implicitly influence research and practice. Through these applications, researchers found hidden views, understood opinions in depth and discovered points of consensus that facilitated unlocking difficult disagreements. The Q methodology has a clear procedure but is also flexible, allowing researchers explore long-term views, or views about items other than statements, such as landscape images. We also found some inconsistencies in applying and, mainly, in reporting Q studies, whereby it was not possible to fully understand how the research was conducted or why some atypical research decisions had been taken in some studies. Accordingly, we suggest a reporting checklist.NM was funded by NERC grant (NE/R006946/1), Fondation Wiener Anspach and Scriven post-doctoral fellowship

    Comparison of techniques for eliciting views and judgements in decision-making

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    1. Decision‐making is a complex process that typically includes a series of stages: identifying the issue, considering possible options, making judgements and then making a decision by combining information and values. The current status quo relies heavily on the informational aspect of decision‐making with little or no emphasis on the value positions that affect decisions. 2. There is increasing realization of the importance of adopting rigorous methods for each stage such that the information, views and judgements of stakeholders and experts are used in a systematic and repeatable manner. Though there are several methodological textbooks which discuss a plethora of social science techniques, it is hard to judge the suitability of any given technique for a given decision problem. 3. In decision‐making, the three critical aspects are “what” decision is to be made, “who” makes the decisions and “how” the decisions are made. The methods covered in this paper focus on “how” decisions can be made. We compare six techniques: Focus Group Discussion (FGD), Interviews, Q methodology, Multi‐criteria Decision Analysis (MCDA), Nominal Group Technique and the Delphi technique specifically in the context of biodiversity conservation. All of these techniques (with the exception of MCDA) help in understanding human values and the underlying perspectives which shape decisions. 4. Based on structured reviews of 423 papers covering all six methods, we compare the conceptual and logistical characteristics of the methods, and map their suitability for the different stages of the decision‐making process. While interviews and FGD are well‐known, techniques such the Nominal Group technique and Q methodology are relatively under‐used. In situations where conflict is high, we recommend using the Q methodology and Delphi technique to elicit judgements. Where conflict is low, and a consensus is needed urgently, the Nominal Group technique may be more suitable. 5. We present a nuanced synthesis of methods aimed at users. The comparison of the different techniques might be useful for project managers, academics or practitioners in the planning phases of their projects and help in making better informed methodological choices.N.M. was funded by the Fondation Wiener Anspach and the Scriven post doctoral fellowship. J.H. is funded by the Belgian National Fund for Research (FRS‐FNRS) and the KLIMOS‐ACROPOLIS project. N.T.O. was funded by Cambridge Overseas Trusts, The Wildlife Conservation Society, Wildlife Conservation Network and WildiZe Foundation. B.A.E. is funded by EU Horizon 2020 ESMERALDA Project, grant agreement no. 642007. W.J.S. is funded by Arcadia

    Bootstrapping Q Methodology to Improve the Understanding of Human Perspectives.

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    Q is a semi-qualitative methodology to identify typologies of perspectives. It is appropriate to address questions concerning diverse viewpoints, plurality of discourses, or participation processes across disciplines. Perspectives are interpreted based on rankings of a set of statements. These rankings are analysed using multivariate data reduction techniques in order to find similarities between respondents. Discussing the analytical process and looking for progress in Q methodology is becoming increasingly relevant. While its use is growing in social, health and environmental studies, the analytical process has received little attention in the last decades and it has not benefited from recent statistical and computational advances. Specifically, the standard procedure provides overall and arguably simplistic variability measures for perspectives and none of these measures are associated to individual statements, on which the interpretation is based. This paper presents an innovative approach of bootstrapping Q to obtain additional and more detailed measures of variability, which helps researchers understand better their data and the perspectives therein. This approach provides measures of variability that are specific to each statement and perspective, and additional measures that indicate the degree of certainty with which each respondent relates to each perspective. This supplementary information may add or subtract strength to particular arguments used to describe the perspectives. We illustrate and show the usefulness of this approach with an empirical example. The paper provides full details for other researchers to implement the bootstrap in Q studies with any data collection design
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